Massive multiple-input multiple-output (MIMO) systems require downlink channel state information (CSI) at the base station (BS) to achieve spatial diversity and multiplexing gains. In a frequency division duplex (FDD) multiuser massive MIMO network, each user needs to compress and feedback its downlink CSI to the BS. The CSI overhead scales with the number of antennas, users and subcarriers, and becomes a major bottleneck for the overall spectral efficiency. In this paper, we propose a deep learning (DL)-based CSI compression scheme, called DeepCMC , composed of convolutional layers followed by quantization and entropy coding blocks. In comparison with previous DL-based CSI reduction structures, DeepCMC proposes a novel fully-convolutional neural network (NN) architecture, with residual layers at the decoder, and incorporates quantization and entropy coding blocks into its design. DeepCMC is trained to minimize a weighted rate-distortion cost, which enables a trade-off between the CSI quality and its feedback overhead. Simulation results demonstrate that DeepCMC outperforms the state of the art CSI compression schemes in terms of the reconstruction quality of CSI for the same compression rate. We also propose a distributed version of DeepCMC for a multi-user MIMO scenario to encode and reconstruct the CSI from multiple users in a distributed manner. Distributed DeepCMC not only utilizes the inherent CSI structures of a single MIMO user for compression, but also benefits from the correlations among the channel matrices of nearby users to further improve the performance in comparison with DeepCMC. We also propose a reduced-complexity training method for distributed DeepCMC, allowing to scale it to multiple users, and suggest a cluster-based distributed DeepCMC approach for practical implementation.

中文翻译：

---

大规模MIMO CSI反馈的分布式深度卷积压缩

大规模多输入多输出（MIMO）系统需要基站（BS）的下行链路信道状态信息（CSI），以实现空间分集和多路复用增益。在频分双工（FDD）多用户大规模MIMO网络中，每个用户都需要压缩其下行链路CSI并将其反馈给BS。CSI开销随天线，用户和子载波的数量而扩展，并成为整个频谱效率的主要瓶颈。在本文中，我们提出了一种基于深度学习（DL）的CSI压缩方案，称为深度CMC 包括卷积层，然后是量化和熵编码块。与以前的基于DL的CSI缩减结构相比，DeepCMC提出了一种新颖的全卷积神经网络（NN）体系结构，在解码器处具有残差层，并将量化和熵编码块纳入其设计。DeepCMC经过培训，可最大程度降低加权速率失真成本，从而在CSI质量与其反馈开销之间进行权衡。仿真结果表明，对于相同的压缩率，DeepCMC在CSI的重建质量方面优于最新的CSI压缩方案。我们还为多用户MIMO方案提出了DeepCMC的分布式版本，以分布式方式对多个用户的CSI进行编码和重构。与DeepCMC相比，分布式DeepCMC不仅利用单个MIMO用户的固有CSI结构进行压缩，而且还受益于附近用户的信道矩阵之间的相关性，从而进一步提高了性能。我们还为分布式DeepCMC提出了一种降低复杂性的训练方法，允许将其扩展到多个用户，并提出了一种基于集群的分布式DeepCMC方法进行实际实施。